Falls are the leading cause of fatal and nonfatal injuries among older adults (>65 years) and result in $30 billion in annual medical costs. Medical alert devices, commonly worn as a pendant, can be used to signal for help in the event of a fall. More recently, medical alert devices with automatic fall detection functionality have been developed. These devices use accelerometry to detect a fall and can signal for help if the wearer forgets to, or is incapable of, pressing the alert button. Widespread adoption of these devices has been limited by the prevalence of undetected falls and false alerts, and by the lack of publically available studies documenting the sensitivity and false alarm rate of commercially-available fall detection devices under real-world settings. BioSensics, in collaboration with the Interdisciplinary Consortium on Advanced Motion Performance and the Arizona Center on Aging at the University of Arizona, developed a medical alert pendant (ActivePERSTM) with automatic fall detection, activity monitoring, and non-compliance alerts through a Phase I & II STTR from the National Institute on Aging. ActivePERS was developed using data from simulated falls and simulated activities of daily living in a laboratory setting. In this setting, ActivePERS has 100% sensitivity and specificity. However, these fall detection algorithms have not been adequately characterized under real-world conditions. The primary objectives of this proposal are to test ActivePERS in a real-world setting, and to improve the ActivePERS fall detection algorithm to achieve an optimal trade-off between sensitivity and false alarm rate, based on acceleration data from real-world falls. In addition, we intend to extend the use of ActivePERS to the detection of near falls. The detection of near falls could enable novel outcome measures aimed to evaluate the effectiveness of interventions designed to achieve a decrease in falls and near falls in older adults. To accomplish these objectives, 200 community-dwelling older adults will wear a fall detection sensor for a period of 12 months. The sensor will be configured to detect falls based on existing algorithms, as well as to record raw tri-axial accelerometer signals for th purposes of algorithm improvements and development of a novel algorithm to detect near falls. Detected falls and near falls will be compared to self-reported falls and near falls. This ambitiou project would not typically be possible given the budget constraints of a Phase II SBIR. However, the present proposal represents a unique partnership between BioSensics and Partners Healthcare (the largest healthcare provider in New England). Partners Healthcare is a trial site in an ongoing, multi-site, $30 million research grant, funded by the Patient-Centered Outcomes Research Institute (PCORI), to find effective and evidence-based strategies for falls prevention. By leveraging the extensive ongoing patient recruitment and relying on the ongoing study for collection of self-reported fall logs, we will be able to achieve the stated objectives within the SBIR budget constraints. The proposed study will provide the largest dataset to date of real-world falls and uniquely position BioSensics to commercialize a reliable fall detection technology.